International Journal of Robotics Research
Autonomous Robots
Understanding intelligence
Biped Locomotion
Framsticks: Towards a Simulation of a Nature-Like World, Creatures and Evolution
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Encoding Scheme Issues for Open-Ended Artificial Evolution
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Cellular encoding for interactive evolutionary robotics
Cellular encoding for interactive evolutionary robotics
Evolving 3d morphology and behavior by competition
Artificial Life
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
The morphofunctional approach to emotion modelling in robotics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Emotion as morphofunctionality
Artificial Life
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In this paper, locomotion of a biped robot operating in a physics-based virtual environment is evolved using a genetic algorithm, in which some of the morphological and control parameters of the system are under evolutionary control. It is shown that stable walking is achieved through coupled optimization of both the controller and the mass ratios and mass distributions of the biped. It was found that although the size of the search space is larger in the case of coupled evolution of morphology and control, these evolutionary runs outperform other runs in which only the biped controller is evolved. We argue that this performance increase is attributable to extradimensional bypasses, which can be visualized as adaptive ridges in the fitness landscape that connect otherwise separated, sub-optimal adaptive peaks. In a similar study, a different set of morphological parameters are included in the evolutionary process. In this case, no significant improvement is gained by coupled evolution. These results demonstrate that the inclusion of the correct set of morphological parameters improves the evolution of adaptive behaviour in simulated agents.